4 research outputs found
Mathematically Modeling the Lexicon Entropy of Emergent Language
We formulate a stochastic process, FiLex, as a mathematical model of lexicon
entropy in deep learning-based emergent language systems. Defining a model
mathematically allows it to generate clear predictions which can be directly
and decisively tested. We empirically verify across four different environments
that FiLex predicts the correct correlation between hyperparameters (training
steps, lexicon size, learning rate, rollout buffer size, and Gumbel-Softmax
temperature) and the emergent language's entropy in 20 out of 20
environment-hyperparameter combinations. Furthermore, our experiments reveal
that different environments show diverse relationships between their
hyperparameters and entropy which demonstrates the need for a model which can
make well-defined predictions at a precise level of granularity.Comment: 12 pages, 3 figures; added link to GitHub rep
Detecting Compromised Implicit Association Test Results Using Supervised Learning
An implicit association test is a human psychological test used to measure
subconscious associations. While widely recognized by psychologists as an
effective tool in measuring attitudes and biases, the validity of the results
can be compromised if a subject does not follow the instructions or attempts to
manipulate the outcome. Compared to previous work, we collect training data
using a more generalized methodology. We train a variety of different
classifiers to identify a participant's first attempt versus a second possibly
compromised attempt. To compromise the second attempt, participants are shown
their score and are instructed to change it using one of five randomly selected
deception methods. Compared to previous work, our methodology demonstrates a
more robust and practical framework for accurately identifying a wide variety
of deception techniques applicable to the IAT.Comment: 6 pages, 1 figur